Recent findings show that 94% of business leaders view AI as crucial to success, but many organizations struggle to move beyond the pilot phase. While initial experiments may demonstrate potential, scaling these initiatives to achieve enterprise-wide impact is a major leadership challenge. This failure often arises from attempting to build advanced systems on outdated, fragmented data foundations.
In this episode of the Analytics Insight Podcast, host Priya Dialani welcomes Marco Diciolla, Vice President at RelationalAI. With a career spanning high-impact roles at Palantir and Quantum Black, Marco shares how RelationalAI is on a mission to power every enterprise decision with intelligence. He explains why CIOs must shift from a ‘data-first’ to a ‘knowledge-first’ approach to capitalize on the true benefits of the modern AI revolution.
RelationalAI aims to bring ‘Decision Intelligence’ to the modern cloud data stack. In a nutshell, this means using technology to automate and support business decisions rather than just storing information. Marco emphasizes that for technology to support a business, it must actually understand that business. By pushing capabilities like business semantics, graph structures, and rules into the infrastructure layer, RelationalAI makes it easier for enterprises to build intelligent applications that solve real-world problems.
Marco notes that while tools like ChatGPT have transformed the consumer world, the ‘ChatGPT moment’ for the enterprise hasn't happened yet. This is because 80% of the world’s data is private. Large Language Models (LLMs) are plateauing because they lack access to this private enterprise context. A knowledge-first approach captures how a specific business thinks, defining what profit means or how a fiscal quarter is structured and surfaces that knowledge to the AI. This results in more reliable answers, lower compute costs, and systems that truly understand the organization they serve.
Many leaders struggle with having ‘more pilots than an airline,’ where experiments are trapped in silos. Marco identifies three traits of leaders who successfully scale AI:
Decision Anchoring: They start with a specific business decision they want to empower and set clear metrics for success.
True Ownership: They ensure there is an internal owner who is incentivized to see the project succeed, rather than just letting a vendor run a solo experiment.
Field Over Lab: They avoid synthetic lab data and instead solve small but meaningful problems in real-life operations immediately to prove actual value.
While the Modern Data Stack has done a good job of making data accessible, knowledge silos are the common enemy. Knowledge is currently scattered across spreadsheets, application code, and the minds of long-term employees. Marco suggests that leaders must start small by capturing structured knowledge from databases and incrementally building a Knowledge Graph. This foundation acts as the textbook for AI agents, teaching these digital interns how the company actually functions.
Ultimately, AI should not be treated as a side project but as a core operating capability. By shifting the focus from do we have enough data? To do so, we understand our knowledge. Enterprises can create an ecosystem where everyone, human and AI, operates cohesively. This shift is the real inflection point for organizations looking to move from mere experimentation to massive industrial impact.
Listen to the full discussion on the Analytics Insight Podcast to learn how to prepare your organization's knowledge for the era of agentic AI.
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